Vision Based Hovering and Landing System for a VTOL-MAV with Geo-Localization Capabilities

Frietsch, Natalie; Meister, Oliver; Schlaile, Christian; Trommer, Gert F.

doi:10.2514/6.2008-6669

Cited by 12 publications

(7 citation statements)

References 12 publications

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“…The visual odometry system presented in Dille et al (2009) shows results of extended range and duration, however the system is implemented on a ground vehicle. Frietsch and Trommer () presented a visual odometry system that enables a vehicle to maintain stability in the absence of GPS, but their system does not demonstrate the long‐distance flight capability that we show in this paper. Furthermore, they did not present flight‐test results.…”

Section: Resultsmentioning

confidence: 71%

“…Leading techniques for autonomous landing site selection and landing, such as in Chamberlain et al (2011), rely on using GPS‐INS navigation solutions and scanning range sensors for landing site selection. Frietsch et al presented an application of vision to the hovering and landing of a quad‐rotor UAV (Frietsch and Trommer, ). In that paper, it was assumed that the camera's motion was dominated by rotation and that the scene was planar.…”

Section: Introductionmentioning

confidence: 99%

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GPS‐denied Indoor and Outdoor Monocular Vision Aided Navigation and Control of Unmanned Aircraft

Chowdhary

Johnson

Magree

et al. 2013

Journal of Field Robotics

147

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GPS‐denied closed‐loop autonomous control of unstable Unmanned Aerial Vehicles (UAVs) such as rotorcraft using information from a monocular camera has been an open problem. Most proposed Vision aided Inertial Navigation Systems (V‐INSs) have been too computationally intensive or do not have sufficient integrity for closed‐loop flight. We provide an affirmative answer to the question of whether V‐INSs can be used to sustain prolonged real‐world GPS‐denied flight by presenting a V‐INS that is validated through autonomous flight‐tests over prolonged closed‐loop dynamic operation in both indoor and outdoor GPS‐denied environments with two rotorcraft unmanned aircraft systems (UASs). The architecture efficiently combines visual feature information from a monocular camera with measurements from inertial sensors. Inertial measurements are used to predict frame‐to‐frame transition of online selected feature locations, and the difference between predicted and observed feature locations is used to bind in real‐time the inertial measurement unit drift, estimate its bias, and account for initial misalignment errors. A novel algorithm to manage a library of features online is presented that can add or remove features based on a measure of relative confidence in each feature location. The resulting V‐INS is sufficiently efficient and reliable to enable real‐time implementation on resource‐constrained aerial vehicles. The presented algorithms are validated on multiple platforms in real‐world conditions: through a 16‐min flight test, including an autonomous landing, of a 66 kg rotorcraft UAV operating in an unconctrolled outdoor environment without using GPS and through a Micro‐UAV operating in a cluttered, unmapped, and gusty indoor environment. © 2013 Wiley Periodicals, Inc.

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Section: Resultsmentioning

confidence: 71%

Section: Introductionmentioning

confidence: 99%

GPS‐denied Indoor and Outdoor Monocular Vision Aided Navigation and Control of Unmanned Aircraft

Chowdhary

Johnson

Magree

et al. 2013

Journal of Field Robotics

147

View full text Add to dashboard Cite

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“…In the early work (Amidi, 1996) it was shown how a visual odometry is capable of stabilizing an autonomous helicopter in hovering conditions. Recent works (Frietsch et al, 2008, Michaelsen et al, 2004, Caballero et al, 2005, Ready and Taylor, 2007 on visual odometry for airborne applications are based on homography estimation under a planar scene assumption. In this case the relation between points of two images can be expressed as x2 ∝ Hx1 where x1 and x2 are the corresponding points of two images 1 and 2 expressed in homogeneous coordinates and H is the 3x3 homography matrix.…”

Section: Visual Odometrymentioning

confidence: 99%

A Visual Navigation System for Uas Based on Geo-Referenced Imagery

Conte

Doherty

2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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This article presents an approach to the terrain-aided navigation problem suitable for unmanned aerial vehicles flying at low altitude. The problem of estimating the state parameters of a flying vehicle is addressed in the particular situation where the GPS information is unavailable or unreliable due to jamming situations for instance. The proposed state estimation approach fuses information from inertial and image sensors. Absolute localization is obtained through image-to-map registration. For this purpose, 2D satellite images are used. The algorithms presented are implemented and tested on-board an industrial unmanned helicopter. Flight-test results will be presented. INTRODUCTIONOperating Unmanned Aerial Systems (UAS) in non-segregated or civil airspace represents a technical challenge. Several problems have to be solved in order to safely operate such systems in populated areas. As an example, an important problem is represented by the capability of an UAS to autonomously perceive and avoid obstacles. Such characteristic is often addressed in the literature as the "See and Avoid" capability. A general robust solution to this problem is non trivial.Another important problem, which is the focus of this article, is related to GPS vulnerability. Modern GPS receivers are capable of providing global position and velocity information with high accuracy. Such information is of vital importance for autonomous navigation for two reasons: it gives an absolute localization means and it can be used to estimate and compensate for the inertial sensors errors. Normally, a UAS navigation system relies on inertial sensors and GPS for estimating the platform states. The main states of a UAS are the position, velocity, acceleration, attitude angles and attitude rates. A precise estimation of the states is essential for accurate autonomous control of the platform. When the GPS is unreliable or unavailable the estimation of the states becomes inaccurate and the UAS autonomous capabilities are not fulfilled.The inertial sensors, or inertial navigation system (INS), are composed of 3 gyroscopes which measure the 3D angular rates, and 3 accelerometers which measure the 3D acceleration. Time integration of the inertial sensors provides platform state information at high rate. The drawback is that, during the integration, the inaccuracies of the inertial sensors cause a drift of the computed states from the real platform states. The drift problem can be solved with an appropriate sensor fusion algorithm combining the INS solution with an external position information. The Kalman filter represents the standard fusing algorithm which optimally solves this kind of state estimation problems.The major concern when using a GPS system is its vulnerability to jamming (Volpe, 2001). The availability of GPS jamming technology on the market makes a UAS unsafe because susceptible to jamming actions. The scope of this article is to propose a solution to the state estimation problem in situations of GPS unavailability. To achieve this goa...

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“…Recent studies on helicopter auto-landings have focused on single rotor type helicopters (9) - (12) and some quad-rotor systems (13), (14) . These studies adopted vision-base approaches for auto-landings which used stereo-vision cameras.…”

Section: Principle Of Position Measurementmentioning

confidence: 99%

Movable Range-Finding Sensor System and Precise Automated Landing of Quad-Rotor MAV

Iwakura

Wang

Nonami

et al. 2011

JSDD

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The accuracy of small and low cost GPS is insufficient to provide data for precisely landing Micro Air Vehicles (MAV)s. This study shows how a MAV can land on a small targeted landing site by using sensors rather than imprecise GPS data. This paper describes a proposed movable range finding sensor system for measuring the environment and an algorithm of position measurement. This range finding system consists of four Infrared (IR) sensors, four servo motors and one ultrasonic sensor. In order to measure the MAV's position, the sensor system vertically swings each IR sensor using the servo motors and these IR sensors detect the edge of landing target. And this sensor system calculates the position from the measured edge direction and the ultrasonic altitude. Additionally, experiments of autonomous hovering over a 52cm × 52cm table and autonomous landings were carried out indoors using the proposed sensor system. Our experiments succeeded, and as a result, the MAV kept flying horizontally within a 18cm radius circle, and then it landed on the table from a height of 50cm. The IR sensors were selected because of the payload limitations of small MAVs. If the MAVs' payload capacity were higher than a laser sensor, then it would be used, since the lasers can operate better in sunlight than IR sensors and also they tend to have longer scanning ranges and are more accurate.

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Vision Based Hovering and Landing System for a VTOL-MAV with Geo-Localization Capabilities

Cited by 12 publications

References 12 publications

GPS‐denied Indoor and Outdoor Monocular Vision Aided Navigation and Control of Unmanned Aircraft

GPS‐denied Indoor and Outdoor Monocular Vision Aided Navigation and Control of Unmanned Aircraft

A Visual Navigation System for Uas Based on Geo-Referenced Imagery

Movable Range-Finding Sensor System and Precise Automated Landing of Quad-Rotor MAV

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